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Found 9 papers, 1 papers with code
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs).
DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrieval
Unlike the existing deep learning approaches that use a neural network as a regularization term or an end-to-end blackbox model for supervised training, our algorithm is a feed-forward neural network implementation of PhaseCut algorithm in an unsupervised learning framework.
Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data
Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-D volume acquisition highly accelerated acquisition is necessary.
Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution
This is because high spatio-temporal resolution ground-truth images are not available for tMRA.
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image.
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS
However, the computation of the divergence term in SURE is difficult to implement in a neural network framework, and the condition to avoid trivial identity mapping is not well defined.
Boosting CNN beyond Label in Inverse Problems
In this paper, we show that the recent unsupervised learning methods such as Noise2Noise, Stein's unbiased risk estimator (SURE)-based denoiser, and Noise2Void are closely related to each other in their formulation of an unbiased estimator of the prediction error, but each of them are associated with its own limitations.
k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI).
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.
